Shell scripts often fail due to unquoted variables, missing error handling, and unsafe patterns. This skill provides production-ready templates, security-aware best practices, and modern command-line alternatives to help you write maintainable Bash and Zsh scripts.

Complex scientific Python dependencies slow down research. Pixi unifies conda and PyPI ecosystems with fast dependency resolution, reproducible lockfiles, and seamless environment management for scientific computing projects.

Software breaks when requirements change because tests are missing or inadequate. This skill provides a disciplined red-green-refactor methodology that ensures every behavior change has verified test coverage before code is written.

Stop guessing at fixes and start finding real root causes. This skill provides a systematic debugging methodology that prevents wasted time on symptom fixes and repeated failed attempts.

Code reviews catch bugs and quality issues before they reach production. This skill provides structured prompts and checklists for AI-powered code review of pull requests and merge requests.

Complex multi-task implementations often suffer from scope creep and quality issues. This skill provides a structured workflow with fresh subagents per task and dual-review gates (spec compliance then code quality) with diff-based validation to ensure every task is correctly implemented.

Code review feedback often contains suggestions that may be unclear, technically questionable, or scope-expanding. This skill provides a systematic approach to evaluate feedback before implementing, ask clarifying questions when needed, and push back with technical reasoning when suggestions are wrong.

Fixing multiple unrelated test failures wastes time when done sequentially. This skill provides a proven workflow to identify independent problems, dispatch focused agents in parallel, and integrate fixes efficiently without conflicts.

Development plans often fail when executed without structure or verification. This skill provides a disciplined methodology for implementing plans with batch processing and review checkpoints.

Automatically detect bugs, security vulnerabilities, and quality issues in your code changes. This skill integrates CodeRabbit CLI to provide intelligent code review feedback directly in your workflow.

Code reviews generate actionable feedback but fixing each issue manually takes time. This skill fetches CodeRabbit review threads from GitHub and applies fixes automatically with your approval.

Agents produce inconsistent output that fails downstream validation. This skill provides a standardized pattern for validating structured JSON against defined schemas before writing final output, ensuring data quality.

Running multiple AI tasks sequentially wastes time. This pattern lets you spawn parallel subagents to work concurrently, dramatically reducing total execution time for independent tasks.

Catch issues early with an iterative testing workflow. Run tests, lint, and typecheck after each code change before declaring work complete.

Writing tests is essential but running them manually wastes time. This skill provides ready-to-use commands for executing test suites with Bun for JavaScript or pytest for Python projects.

This skill provides access to common code patterns and abstractions used in your project. It ensures consistent implementation across features by loading established patterns for data access, API design, components, state management, and error handling.

Code quality varies across projects and team members. This skill provides standardized linting commands for TypeScript and Python that you can customize and run consistently across your codebase.

Parallel AI agents often conflict when editing the same files. This skill teaches agents to recognize and respect ownership boundaries, preventing lost work and merge conflicts during collaborative implementation.

Understanding large codebases takes too long when done sequentially. This skill provides patterns for spawning parallel explorer subagents to gather context quickly and produce structured summaries.

Subagents often fail due to timeouts, unclear requirements, or scope issues. This skill provides proven retry patterns to recover from failures automatically.